A method, device, equipment and medium for MPPT control based on a photovoltaic array

Abstract

The application relates to the technical field of solar power generation, and discloses an MPPT control method, device and equipment based on a photovoltaic array and a medium. The method comprises the following steps: acquiring environment data and photovoltaic output data of the photovoltaic array; acquiring a target stability coefficient and target tracking data according to a preset deep learning training model; determining a voltage reference value based on the photovoltaic output data, the target tracking data and a preset reference voltage; searching for a first target control parameter associated with the target stability coefficient and a second target control parameter associated with the target stability coefficient; determining a current reference value of a current control loop based on the voltage reference value; and determining a target output duty cycle of a pulse signal based on the current reference value, the photovoltaic output data and the second target control parameter, so that the photovoltaic array reaches a target maximum output power. The application can improve the efficiency and precision of maximum power point tracking.

Description

Technical Field

[0001] This application relates to the field of solar power generation technology, and in particular to an MPPT control method, device, equipment and medium based on photovoltaic array. Background Technology

[0002] Currently, given the relatively low conversion efficiency of solar photovoltaic (PV) power generation, maximizing PV power generation efficiency and the utilization rate of solar photovoltaic conversion is one of the challenges facing the promotion and application of PV power generation technology. Existing methods typically use Maximum Power Point Tracking (MPPT) to maximize photovoltaic conversion, but the application of existing MPPT control methods in PV arrays is not mature or perfect, suffering from drawbacks such as slow tracking speed, large steady-state error, and long adjustment time for control parameters. Summary of the Invention

[0003] This application provides a method, apparatus, device, and medium for MPPT control based on a photovoltaic array, to solve one or more technical problems existing in the prior art, and to at least provide a beneficial option or create conditions.

[0004] Other features and advantages of this application will become apparent from the following detailed description, or may be learned in part from practice of this application.

[0005] According to one aspect of the embodiments of this application, an MPPT control method based on a photovoltaic array is provided, the method comprising:

[0006] Acquire environmental data and photovoltaic output data of the photovoltaic array;

[0007] Based on the photovoltaic output data, the environmental data, and the preset deep learning training model, obtain the target stability coefficient and target tracking data associated with the photovoltaic output data and the environmental data;

[0008] The voltage reference value of the voltage control loop is determined based on the photovoltaic output data, the target tracking data, and the preset reference voltage.

[0009] Find the first target control parameter associated with the target stability coefficient in the preset coefficient-voltage control parameter mapping table, and find the second target control parameter associated with the target stability coefficient in the preset coefficient-current control parameter mapping table;

[0010] The current reference value of the current control loop is determined based on the first target control parameter, the photovoltaic output data, and the voltage reference value;

[0011] The target output duty cycle of the pulse signal is determined based on the current reference value, the photovoltaic output data, and the second target control parameter, so that the inverter can control the photovoltaic output data of the photovoltaic array according to the pulse signal, so that the photovoltaic array can reach the target maximum output power.

[0012] In one embodiment of this application, based on the foregoing scheme, the photovoltaic output data includes the output voltage, output current, and output power of the photovoltaic array; the environmental data includes illuminance and temperature; and the target tracking data includes the target tracking voltage range and the target tracking step size. The step of obtaining the target stability coefficient and target tracking data based on the photovoltaic output data, the environmental data, and a preset deep learning training model includes:

[0013] The output voltage, output current, and output power are normalized and subjected to hyperbolic tangent transformation to obtain the target voltage parameter, target current parameter, and target power parameter.

[0014] The target voltage parameter, the target current parameter, the target power parameter, the illuminance, and the temperature are input into the deep learning training model to obtain the target stability coefficient, the target tracking voltage range, and the target tracking step size associated with the target voltage parameter, the target current parameter, the target power parameter, the illuminance, and the temperature.

[0015] The output power can be calculated based on the output voltage and the output current.

[0016] In one embodiment of this application, based on the foregoing scheme, determining the voltage reference value of the voltage control loop based on the photovoltaic output data, the target tracking data, and a preset reference voltage includes:

[0017] The previous output power of the photovoltaic array at the previous moment is obtained based on the photovoltaic output data, and the perturbation attribute of the target tracking step size is determined based on the previous output power and the current output power.

[0018] The voltage reference value is determined based on the disturbance attribute, the target tracking voltage range, and the reference voltage.

[0019] In one embodiment of this application, based on the foregoing scheme, the perturbation attribute includes a perturbation step size and a perturbation step size attribute, and determining the voltage reference value based on the perturbation attribute, the target tracking voltage range, and the reference voltage includes:

[0020] Determine whether the sum of the perturbation step size and the reference voltage is within the target tracking voltage range. If so, use the sum of the perturbation step size and the reference voltage as the voltage reference value.

[0021] If the sum of the perturbation step size and the reference voltage is not within the target tracking voltage range, then the maximum / minimum value in the target tracking voltage range is selected as the voltage reference value based on the perturbation step size attribute.

[0022] The unit of the perturbation step size is voltage, and the perturbation step size attribute is used to characterize the relationship between the target tracking step size and the output voltage.

[0023] In one embodiment of this application, based on the foregoing scheme, the step of normalizing and hyperbolic tangent transforming the output voltage, the output current, and the output power to obtain target voltage parameters, target current parameters, and target power parameters includes:

[0024] The output voltage is normalized to a preset rated voltage value to obtain a voltage per-unit value;

[0025] The output current is normalized to a preset rated current value to obtain a per-unit current value;

[0026] The output power is normalized to a preset rated power value to obtain a power per-unit value;

[0027] The per-unit values ​​of voltage, current, and power are each subjected to a hyperbolic tangent transform to obtain the target voltage parameter, the target current parameter, and the target power parameter after hyperbolic tangent function transformation.

[0028] In one embodiment of this application, based on the foregoing scheme, determining the current reference value of the current control loop based on the first target control parameter, the photovoltaic output data, and the voltage reference value includes:

[0029] Based on the voltage error between the output voltage and the voltage reference value and the first target control parameter, a PID calculation is performed to obtain the current reference value of the current control loop.

[0030] In one embodiment of this application, based on the foregoing scheme, determining the target output duty cycle of the pulse signal based on the current reference value, the photovoltaic output data, and the second target control parameter includes:

[0031] Based on the current error value between the output current and the current reference value and the second target control parameter, a PID calculation is performed to obtain the target output duty cycle of the pulse signal, so that the inverter can control the output current and output voltage of the photovoltaic array according to the pulse signal, so that the photovoltaic array can reach the target maximum output power.

[0032] According to one aspect of the embodiments of this application, an MPPT control device based on a photovoltaic array is provided, the device comprising:

[0033] The first acquisition unit is used to acquire environmental data and photovoltaic output data of the photovoltaic array;

[0034] The second acquisition unit is used to acquire the target stability coefficient and target tracking data associated with the photovoltaic output data and the environmental data based on the photovoltaic output data, the environmental data and the preset deep learning training model;

[0035] The first determining unit is used to determine the voltage reference value of the voltage control loop based on the photovoltaic output data, the target tracking data, and the preset reference voltage.

[0036] The lookup unit is used to look up a first target control parameter associated with the target stability coefficient in a preset coefficient-voltage control parameter mapping table, and to look up a second target control parameter associated with the target stability coefficient in a preset coefficient-current control parameter mapping table;

[0037] The second determining unit is used to determine the current reference value of the current control loop based on the first target control parameter, the photovoltaic output data and the voltage reference value;

[0038] The control unit is configured to determine the target output duty cycle of the pulse signal based on the current reference value, the photovoltaic output data, and the second target control parameter, so that the inverter can control the photovoltaic output data of the photovoltaic array according to the pulse signal, so that the photovoltaic array can reach the target maximum output power.

[0039] According to one aspect of the embodiments of this application, a computer-readable storage medium is provided, on which a computer program is stored, the computer program including executable instructions that, when executed by a processor, implement the control method as described in the above embodiments.

[0040] According to one aspect of the embodiments of this application, an electronic device is provided, including: one or more processors; and a memory for storing executable instructions of the processors, which, when executed by the one or more processors, cause the one or more processors to implement the control method as described in the above embodiments.

[0041] The beneficial effects of this application are as follows: By inputting photovoltaic output data and environmental data into a preset deep learning training model, the voltage reference value of the voltage control loop is determined based on the target tracking data output by the deep learning training model, combined with the photovoltaic output data and a preset reference voltage. Simultaneously, a first target control parameter and a second target control parameter are determined through the target stability coefficient, and a current reference value of the current control loop is determined based on the first target control parameter, the photovoltaic output data, and the voltage reference value.

[0042] Finally, based on the obtained current reference value, the acquired photovoltaic output data, and the second target control parameters, the target output duty cycle of the pulse signal is determined. This allows the inverter to control the photovoltaic output data of the photovoltaic array according to the pulse signal, enabling the photovoltaic array to reach the target maximum output power. The MPPT control method provided in this application can quickly predict accurate voltage and current reference values ​​based on the target stability coefficient and target tracking data obtained from the deep learning training model. Finally, based on the current reference value, the current of the photovoltaic array is controlled, allowing the photovoltaic array to quickly reach the corresponding target voltage according to the current adjustment value. This, in turn, enables the photovoltaic array to quickly reach the maximum power point at the target voltage, improving the maximum power point tracking efficiency and accuracy. Attached Figure Description

[0043] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly explained below. Obviously, the described drawings are only a part of the embodiments of this application, and not all of them. Those skilled in the art can obtain other design schemes and drawings based on these drawings without creative effort.

[0044] Figure 1 This is a flowchart illustrating a photovoltaic array-based MPPT control method according to an embodiment of this application;

[0045] Figure 2 This is an overall logic diagram of the MPPT control method based on a photovoltaic array, as shown in the embodiments of this application.

[0046] Figure 3 This is a block diagram of a photovoltaic array-based MPPT control device according to an embodiment of this application;

[0047] Figure 4 This is a schematic diagram of the system structure of an electronic device according to an embodiment of this application. Detailed Implementation

[0048] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided to make this application more comprehensive and complete, and to fully convey the concept of the exemplary embodiments to those skilled in the art.

[0049] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a thorough understanding of embodiments of this application. However, those skilled in the art will recognize that the technical solutions of this application can be practiced without one or more of the specific details, or other methods, components, apparatuses, steps, etc., can be employed. In other instances, well-known methods, apparatuses, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of this application.

[0050] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller node devices.

[0051] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily have to be performed in the described order. For example, some operations / steps can be broken down, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0052] It should be noted that "multiple" in this article refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. The character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0053] The implementation details of the technical solutions in the embodiments of this application are described in detail below:

[0054] First, it should be noted that the preset deep learning training model is generated through training on a large amount of sample data. The input sample types fall into three main categories: photovoltaic output data (photovoltaic parameters), illuminance, and temperature. Photovoltaic output data further includes the output current, output voltage, and output power of the photovoltaic array. By using multiple parameters as sample data to train the deep learning model, it can quickly and accurately output the desired results based on the input parameters.

[0055] The deep learning training model in this embodiment is a model-free adaptive control algorithm model for multi-input multi-output systems based on compact-format dynamic linearization. It has several advantages, such as: no need for precise understanding of the control process; no need for the system to include an identification mechanism; no need for manual parameter tuning; and guaranteed stability of the closed-loop system. Therefore, multiple input parameters can be quickly and adaptively adjusted to output corresponding results.

[0056] However, in the embodiments of this application, the output of the deep learning training model is not the maximum power point tracking voltage, but rather the target tracking voltage range, target tracking step size, and target stability coefficient of the photovoltaic array. The target tracking step size is the MPPT algorithm step size. The voltage reference range and MPPT algorithm step size are used to obtain the voltage reference value more accurately, enabling the photovoltaic array to quickly reach the voltage reference value and thus complete the tracking of the maximum power point.

[0057] After obtaining the voltage reference value, the photovoltaic array cannot be directly controlled to reach the voltage reference value. Instead, the on and off time of the IGBT (Insulated Gate Bipolar Transistor) in the inverter needs to be controlled by a pulse signal to adjust the output current of the photovoltaic array. After the output current is adjusted, the output voltage of the photovoltaic array can also be adjusted. Then, based on the parameter values, the output voltage of the photovoltaic array can be made to reach the voltage reference value, thereby completing the maximum power point tracking.

[0058] According to one aspect of the embodiments of this application, an MPPT control method based on a photovoltaic array is provided. Figure 1 The flowchart below illustrates a photovoltaic array-based MPPT control method according to an embodiment of this application. The method includes at least steps 110 to 160, which are described in detail below:

[0059] In step 110, environmental data and photovoltaic output data of the photovoltaic array are acquired.

[0060] Specifically, environmental data includes illuminance and temperature, while photovoltaic output data includes the output voltage, output current, and output power of the photovoltaic array. By inputting these five parameters—output voltage, output current, output power, illuminance, and temperature—into a pre-trained deep learning model, the corresponding target stability coefficient and target tracking data can be quickly output. The target tracking data includes the target tracking voltage range and the target tracking step size.

[0061] In step 120, target stability coefficients and target tracking data associated with the photovoltaic output data and the environmental data are obtained based on the photovoltaic output data, the environmental data, and a preset deep learning training model.

[0062] In one embodiment of this application, the photovoltaic output data includes the output voltage, output current, and output power of the photovoltaic array; the environmental data includes illuminance and temperature; and the target tracking data includes the target tracking voltage range and the target tracking step size. The step of obtaining the target stability coefficient and target tracking data based on the photovoltaic output data, the environmental data, and a preset deep learning training model includes:

[0063] The output voltage, output current, and output power are normalized and subjected to hyperbolic tangent transformation to obtain the target voltage parameter, target current parameter, and target power parameter.

[0064] The target voltage parameter, the target current parameter, the target power parameter, the illuminance, and the temperature are input into the deep learning training model to obtain the target stability coefficient, the target tracking voltage range, and the target tracking step size associated with the target voltage parameter, the target current parameter, the target power parameter, the illuminance, and the temperature.

[0065] The output power can be calculated based on the output voltage and the output current.

[0066] Specifically, please refer to Figure 2 As shown, Figure 2 This is the overall logic diagram of the MPPT control method proposed in this application. The output voltage, output current, and output power are each normalized and subjected to hyperbolic tangent transformation to obtain target voltage parameters, target current parameters, and target power parameters. Normalization involves dividing the actual output voltage, output current, and output power by the rated voltage, rated current, and rated power, respectively. The rated voltage, rated current, and rated power are obtained based on experimental experience and can also be set according to actual needs.

[0067] By performing a hyperbolic tangent transform on the normalized parameters, the target voltage, target current, and target power parameters obtained after the transformation can be rapidly adjusted according to environmental data such as illuminance and temperature, thus achieving an adaptive and rapid adjustment effect. This is because the tangent function is monotonically increasing and changes rapidly.

[0068] In one embodiment of this application, the step of normalizing and hyperbolic tangent transforming the output voltage, the output current, and the output power to obtain target voltage parameters, target current parameters, and target power parameters includes:

[0069] The output voltage is normalized to a preset rated voltage value to obtain a voltage per-unit value;

[0070] The output current is normalized to a preset rated current value to obtain a per-unit current value;

[0071] The output power is normalized to a preset rated power value to obtain a power per-unit value;

[0072] The per-unit values ​​of voltage, current, and power are each subjected to a hyperbolic tangent transform to obtain the target voltage parameter, the target current parameter, and the target power parameter after hyperbolic tangent function transformation.

[0073] Specifically, as shown above, the per-unit voltage value is the quotient of the actual output voltage divided by the rated voltage value, the per-unit current value is the quotient of the actual output current divided by the rated current value, and the per-unit power value is the quotient of the actual output power divided by the rated power value.

[0074] The target voltage parameter, target current parameter, target power parameter, illuminance, and temperature are input into the deep learning training model to obtain the target stability coefficient, target tracking voltage range, and target tracking step size associated with the target voltage parameter, target current parameter, target power parameter, illuminance, and temperature. In other words, as shown... Figure 2 As shown in the figure, F1 and F2 are the target stability coefficients, while Vpv_max and Vpv_min are the highest and lowest voltages in a single MPPT tracking, respectively, which together form the target tracking voltage range, and step is the target tracking step size.

[0075] It should be noted that, Figure 2 In this context, C represents the deep learning algorithm, which is the deep learning training model mentioned in the embodiments of this application.

[0076] Furthermore, to better illustrate the calculation and determination method of the voltage reference value, the following is a detailed explanation:

[0077] First, the current voltage reference value is determined by combining the target tracking voltage range, the target tracking step size, and the voltage reference value from the previous moment. If the sum of the previous voltage reference value and the current target tracking step size is within the target tracking voltage range, then the sum of the previous voltage reference value and the current target tracking step size is used as the current voltage reference value. If it is not within the target tracking voltage range, then further explanation is needed in conjunction with the following embodiments:

[0078] In step 130, the voltage reference value of the voltage control loop is determined based on the photovoltaic output data, the target tracking data, and the preset reference voltage.

[0079] Specifically, the voltage reference value of the voltage control loop can be determined by combining the output voltage in the photovoltaic output data, the target tracking voltage range and target tracking step size in the target tracking data with a preset reference voltage. The preset reference voltage can be set as needed, or the voltage reference value given at the previous moment can be used as the preset reference voltage.

[0080] In one embodiment of this application, determining the voltage reference value of the voltage control loop based on the photovoltaic output data, the target tracking data, and a preset reference voltage includes:

[0081] The previous output power of the photovoltaic array at the previous moment is obtained based on the photovoltaic output data, and the perturbation attribute of the target tracking step size is determined based on the previous output power and the current output power.

[0082] The voltage reference value is determined based on the disturbance attribute, the target tracking voltage range, and the reference voltage.

[0083] In one embodiment of this application, the perturbation attribute includes a perturbation step size and a perturbation step size attribute, and determining the voltage reference value based on the perturbation attribute, the target tracking voltage range, and the reference voltage includes:

[0084] Determine whether the sum of the perturbation step size and the reference voltage is within the target tracking voltage range. If so, use the sum of the perturbation step size and the reference voltage as the voltage reference value.

[0085] If the sum of the perturbation step size and the reference voltage is not within the target tracking voltage range, then the maximum / minimum value in the target tracking voltage range is selected as the voltage reference value based on the perturbation step size attribute.

[0086] The unit of the perturbation step size is voltage, and the perturbation step size attribute is used to characterize the relationship between the target tracking step size and the output voltage.

[0087] Specifically, the previous output power of the photovoltaic array at the previous moment is obtained based on the photovoltaic output data, and the perturbation attribute of the target tracking step size is determined based on the previous output power and the current output power. In other words, the perturbation attribute of the target tracking step size can affect the current output power of the photovoltaic array. If the perturbation attribute of the perturbation step size is positive, then increasing the value of the perturbation step size will lead to an increase in the output power of the photovoltaic array. If it is negative, it will lead to a decrease in the output power of the photovoltaic array.

[0088] The perturbation step size represents the magnitude of the step, while the perturbation step size attribute represents the positive or negative value of the step size. Therefore, if the sum of the perturbation step size and the reference voltage is not within the range of the target tracking voltage, the voltage reference value can be determined by combining the perturbation step size attribute. First, if the value of the step size represented by the perturbation step size attribute is positive, then the maximum value within the target tracking voltage range can be selected. Figure 2 Vpv_max is used as the voltage reference value; conversely, if the value of the step size represented by the perturbation step size attribute is negative, then the minimum value in the target tracking voltage range can be selected. Figure 2 Vpv_min is used as the voltage reference value.

[0089] In step 140, a first target control parameter associated with the target stability coefficient is searched in a preset coefficient-voltage control parameter mapping table, and a second target control parameter associated with the target stability coefficient is searched in a preset coefficient-current control parameter mapping table.

[0090] Specifically, the preset coefficient-voltage control parameter mapping table is as follows: Figure 2 In F2, the preset coefficient-current control parameter mapping table is as follows: Figure 2 In the example, F1 and F2 are both pre-tuned nonlinear lookup tables. They allow direct lookup of the target parameters from the target stability coefficient. These target parameters are then used to calculate the final first and second target control parameters using G1 and G2, respectively. G1 and G2 both represent linear interpolation algorithms.

[0091] In step 150, the current reference value of the current control loop is determined based on the first target control parameter, the photovoltaic output data, and the voltage reference value.

[0092] Specifically, the first target control parameter and the second target control parameter are... Figure 2 The controller parameters shown are used, and based on the first target control parameters, the output voltage, and the voltage reference value, a PID calculation can be performed to obtain the current reference value.

[0093] In one embodiment of this application, determining the current reference value of the current control loop based on the first target control parameter, the photovoltaic output data, and the voltage reference value includes:

[0094] Based on the voltage error between the output voltage and the voltage reference value and the first target control parameter, a PID calculation is performed to obtain the current reference value of the current control loop.

[0095] The target stability coefficient is input as an input parameter to the deep learning algorithm C (i.e., the deep learning training model described in this application). Through a linear interpolation algorithm, the pre-tuned nonlinear voltage loop parameter table and current loop parameter table (i.e., the preset coefficient-voltage control parameter mapping table and the preset coefficient-current control parameter mapping table) are consulted to obtain suitable voltage loop Kp and Ki parameters (i.e., the first target control parameters), and current control loop Kp and Ki parameters (i.e., the second target control parameters). The voltage reference value (Vpv*), the actual output voltage value (Vpv), and the voltage control loop Kp and Ki parameters are then used for PI calculation. Figure 2 H), to obtain the current reference value (iL*).

[0096] In step 160, the target output duty cycle of the pulse signal is determined based on the current reference value, the photovoltaic output data, and the second target control parameter, so that the inverter can control the photovoltaic output data of the photovoltaic array according to the pulse signal, so that the photovoltaic array can reach the target maximum output power.

[0097] Specifically, by performing PI calculations using the current reference value, the output current in the photovoltaic output data, and the second target control parameter, the target output duty cycle of the pulse signal can be determined. This allows the inverter to control the output current of the photovoltaic array based on the sent pulse signal, thereby controlling its output voltage to the corresponding voltage reference value, and ultimately enabling the photovoltaic array to reach the target maximum output power.

[0098] In one embodiment of this application, determining the target output duty cycle of the pulse signal based on the current reference value, the photovoltaic output data, and the second target control parameter includes:

[0099] Based on the current error value between the output current and the current reference value and the second target control parameter, a PID calculation is performed to obtain the target output duty cycle of the pulse signal, so that the inverter can control the output current and output voltage of the photovoltaic array according to the pulse signal, so that the photovoltaic array can reach the target maximum output power.

[0100] Specifically, the current reference value (iL*), the actual output current value (iL), and the Kp and Ki parameters of the current control loop are calculated together using PI. Figure 2I) Obtain the output duty cycle d; based on the output duty cycle d, adjust the PWM (pulse signal) output duty cycle, and drive the power switching transistor IGBT through the PWM (pulse signal) to adjust the output current of the photovoltaic array to adjust the output voltage of the photovoltaic array, and thus adjust the output power of the photovoltaic array, so as to continuously approach the MPPT point, so that the photovoltaic array is not affected by some special situations such as drastic changes in light intensity, sudden drop or rise in temperature, and can still continuously track the maximum output power according to its own control strategy.

[0101] In summary, the embodiments of this application input photovoltaic output data and environmental data into a preset deep learning training model. Based on the target tracking data output by the deep learning training model, combined with the photovoltaic output data and a preset reference voltage, a voltage reference value for the voltage control loop is determined. Simultaneously, a first target control parameter and a second target control parameter are determined using a target stability coefficient. Finally, a current reference value for the current control loop is determined based on the first target control parameter, the photovoltaic output data, and the voltage reference value.

[0102] Finally, based on the obtained current reference value, the acquired photovoltaic output data, and the second target control parameters, the target output duty cycle of the pulse signal is determined. This allows the inverter to control the photovoltaic output data of the photovoltaic array according to the pulse signal, enabling the photovoltaic array to reach the target maximum output power. The MPPT control method provided in this application can quickly predict accurate voltage and current reference values ​​based on the target stability coefficient and target tracking data obtained from the deep learning training model. Finally, based on the current reference value, the current of the photovoltaic array is controlled, allowing the photovoltaic array to quickly reach the corresponding target voltage according to the current adjustment value. This, in turn, enables the photovoltaic array to quickly reach the maximum power point at the target voltage, improving the maximum power point tracking efficiency and accuracy.

[0103] Figure 3 The diagram shows a block diagram of a photovoltaic array-based MPPT control device 300 according to an embodiment of this application. The device 300 according to an embodiment of this application includes: a first acquisition unit 301, a second acquisition unit 302, a first determination unit 303, a search unit 304, a second determination unit 305, and a control unit 306.

[0104] The first acquisition unit 301 is used to acquire environmental data and photovoltaic output data of the photovoltaic array;

[0105] The second acquisition unit 302 is used to acquire the target stability coefficient and target tracking data associated with the photovoltaic output data and the environmental data based on the photovoltaic output data, the environmental data and the preset deep learning training model;

[0106] The first determining unit 303 is used to determine the voltage reference value of the voltage control loop based on the photovoltaic output data, the target tracking data and the preset reference voltage;

[0107] The lookup unit 304 is used to look up a first target control parameter associated with the target stability coefficient in a preset coefficient-voltage control parameter mapping table, and to look up a second target control parameter associated with the target stability coefficient in a preset coefficient-current control parameter mapping table;

[0108] The second determining unit 305 is used to determine the current reference value of the current control loop based on the first target control parameter, the photovoltaic output data and the voltage reference value;

[0109] Control unit 306 is used to determine the target output duty cycle of the pulse signal based on the current reference value, the photovoltaic output data and the second target control parameter, so that the inverter can control the photovoltaic output data of the photovoltaic array according to the pulse signal, so that the photovoltaic array can reach the target maximum output power.

[0110] In another aspect, this application also provides a computer-readable storage medium storing a program product capable of implementing the methods provided above in this specification. In some possible implementations, various aspects of this application may also be implemented as a program product comprising program code that, when run on a terminal device, causes the terminal device to perform the steps described in the "Embodiment Methods" section of this specification according to various exemplary embodiments of this application.

[0111] The program product for implementing the above-described method according to the embodiments of this application may employ a portable compact disc read-only memory (CD-ROM) and include program code, and may run on a terminal device, such as a personal computer. However, the program product of this application is not limited thereto. In this document, the readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, apparatus, or device.

[0112] The program product may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0113] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium, capable of sending, propagating, or transmitting programs for use by or in conjunction with an instruction execution system, apparatus, or device.

[0114] The program code contained on the readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0115] Program code for performing the operations of this application can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0116] In another respect, this application also provides an electronic device capable of implementing the above-described method.

[0117] Those skilled in the art will understand that various aspects of this application can be implemented as a system, method, or program product. Therefore, various aspects of this application can be specifically implemented in the following forms: a completely hardware implementation, a completely software implementation (including firmware, microcode, etc.), or a combination of hardware and software implementations, collectively referred to herein as a "circuit," "module," or "system."

[0118] The following reference Figure 4 To describe an electronic device 400 according to this embodiment of the present application. Figure 4 The electronic device 400 shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0119] like Figure 4 As shown, the electronic device 400 is manifested in the form of a general-purpose computing device. The components of the electronic device 400 may include, but are not limited to: at least one processing unit 410, at least one storage unit 420, and a bus 430 connecting different system components (including storage unit 420 and processing unit 410).

[0120] The storage unit stores program code that can be executed by the processing unit 410, causing the processing unit 410 to perform the steps described in the "Embodiment Methods" section above according to various exemplary embodiments of this application.

[0121] Storage unit 420 may include readable media in the form of volatile storage units, such as random access memory (RAM) 421 and / or cache memory 422, and may further include read-only memory (ROM) 423.

[0122] Storage unit 420 may also include a program / utility 424 having a set (at least one) of program modules 425, including but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment.

[0123] Bus 430 can represent one or more of several types of bus structures, including a memory cell bus or memory cell control node, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the various bus structures.

[0124] Electronic device 400 can also communicate with one or more external devices 1200 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with electronic device 400, and / or with any device that enables electronic device 400 to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed via input / output (I / O) interface 450. Furthermore, electronic device 400 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 460. As shown, network adapter 460 communicates with other modules of electronic device 400 via bus 430. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 400, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0125] Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, terminal device, or network device, etc.) to execute the methods according to the embodiments of this application.

[0126] Furthermore, the above figures are merely illustrative of the processes included in the method according to exemplary embodiments of this application, and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Additionally, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0127] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A method for MPPT control based on a photovoltaic array, characterized in that, The method includes: Acquire environmental data and photovoltaic output data of the photovoltaic array; Based on the photovoltaic output data, the environmental data, and the preset deep learning training model, obtain the target stability coefficient and target tracking data associated with the photovoltaic output data and the environmental data; The voltage reference value of the voltage control loop is determined based on the photovoltaic output data, the target tracking data, and the preset reference voltage. Find the first target control parameter associated with the target stability coefficient in the preset coefficient-voltage control parameter mapping table, and find the second target control parameter associated with the target stability coefficient in the preset coefficient-current control parameter mapping table; The current reference value of the current control loop is determined based on the first target control parameter, the photovoltaic output data, and the voltage reference value; The target output duty cycle of the pulse signal is determined based on the current reference value, the photovoltaic output data, and the second target control parameter, so that the inverter can control the photovoltaic output data of the photovoltaic array according to the pulse signal, so that the photovoltaic array can reach the target maximum output power.

2. The control method according to claim 1, characterized in that, The photovoltaic output data includes the output voltage, output current, and output power of the photovoltaic array; the environmental data includes illuminance and temperature; and the target tracking data includes the target tracking voltage range and target tracking step size. The process of obtaining the target stability coefficient and target tracking data based on the photovoltaic output data, the environmental data, and a pre-set deep learning training model includes: The output voltage, output current, and output power are normalized and subjected to hyperbolic tangent transformation to obtain the target voltage parameter, target current parameter, and target power parameter. The target voltage parameter, the target current parameter, the target power parameter, the illuminance, and the temperature are input into the deep learning training model to obtain the target stability coefficient, the target tracking voltage range, and the target tracking step size associated with the target voltage parameter, the target current parameter, the target power parameter, the illuminance, and the temperature. The output power can be calculated based on the output voltage and the output current.

3. The control method according to claim 2, characterized in that, The step of determining the voltage reference value of the voltage control loop based on the photovoltaic output data, the target tracking data, and the preset reference voltage includes: The previous output power of the photovoltaic array at the previous moment is obtained based on the photovoltaic output data, and the perturbation attribute of the target tracking step size is determined based on the previous output power and the current output power. The voltage reference value is determined based on the disturbance attribute, the target tracking voltage range, and the reference voltage.

4. The control method according to claim 3, characterized in that, The perturbation attribute includes a perturbation step size and a perturbation step size attribute. Determining the voltage reference value based on the perturbation attribute, the target tracking voltage range, and the reference voltage includes: Determine whether the sum of the perturbation step size and the reference voltage is within the target tracking voltage range. If so, use the sum of the perturbation step size and the reference voltage as the voltage reference value. If the sum of the perturbation step size and the reference voltage is not within the target tracking voltage range, then the maximum / minimum value in the target tracking voltage range is selected as the voltage reference value based on the perturbation step size attribute. The unit of the perturbation step size is voltage, and the perturbation step size attribute is used to characterize the relationship between the target tracking step size and the output voltage.

5. The control method according to claim 2, characterized in that, The step of normalizing and performing hyperbolic tangent transformation on the output voltage, output current, and output power to obtain target voltage parameters, target current parameters, and target power parameters includes: The output voltage is normalized to a preset rated voltage value to obtain a voltage per-unit value; The output current is normalized to a preset rated current value to obtain a per-unit current value; The output power is normalized to a preset rated power value to obtain a power per-unit value; The per-unit values ​​of voltage, current, and power are each subjected to a hyperbolic tangent transform to obtain the target voltage parameter, the target current parameter, and the target power parameter after hyperbolic tangent function transformation.

6. The control method according to claim 4, characterized in that, The step of determining the current reference value of the current control loop based on the first target control parameter, the photovoltaic output data, and the voltage reference value includes: Based on the voltage error between the output voltage and the voltage reference value and the first target control parameter, a PID calculation is performed to obtain the current reference value of the current control loop.

7. The control method according to claim 6, characterized in that, Determining the target output duty cycle of the pulse signal based on the current reference value, the photovoltaic output data, and the second target control parameter includes: Based on the current error value between the output current and the current reference value and the second target control parameter, a PID calculation is performed to obtain the target output duty cycle of the pulse signal, so that the inverter can control the output current and output voltage of the photovoltaic array according to the pulse signal, so that the photovoltaic array can reach the target maximum output power.

8. An MPPT control device based on a photovoltaic array, characterized in that, The device includes: The first acquisition unit is used to acquire environmental data and photovoltaic output data of the photovoltaic array; The second acquisition unit is used to acquire the target stability coefficient and target tracking data associated with the photovoltaic output data and the environmental data based on the photovoltaic output data, the environmental data and the preset deep learning training model; The first determining unit is used to determine the voltage reference value of the voltage control loop based on the photovoltaic output data, the target tracking data, and the preset reference voltage. The lookup unit is used to look up a first target control parameter associated with the target stability coefficient in a preset coefficient-voltage control parameter mapping table, and to look up a second target control parameter associated with the target stability coefficient in a preset coefficient-current control parameter mapping table; The second determining unit is used to determine the current reference value of the current control loop based on the first target control parameter, the photovoltaic output data and the voltage reference value; The control unit is configured to determine the target output duty cycle of the pulse signal based on the current reference value, the photovoltaic output data, and the second target control parameter, so that the inverter can control the photovoltaic output data of the photovoltaic array according to the pulse signal, so that the photovoltaic array can reach the target maximum output power.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one piece of program code, which is loaded and executed by a processor to perform the operations performed by the method as described in any one of claims 1 to 7.

10. An electronic device, characterized in that, The electronic device includes one or more processors and one or more memories, wherein at least one piece of program code is stored in the one or more memories, and the at least one piece of program code is loaded and executed by the one or more processors to perform the operation performed by the method as described in any one of claims 1 to 7.

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